# dspy-evaluation-suite

Tests and benchmarks DSPy modules against evaluation datasets using multi-threaded execution and custom scoring functions.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · ★ 124
- Author: OmidZamani
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-evaluation-suite/SKILL.md
- Updated: 2026-06-23
- HTML: https://affpapa.org/en/nevibecode/skills/dspy-evaluation-suite

## What it does

This skill automates testing for DSPy pipelines. The `Evaluate` module runs validation examples across parallel worker threads. Preconfigured patterns cover built-in metrics such as `answer_exact_match` and the model-based `SemanticF1`, alongside custom multi-factor scoring functions.

An included `EvaluationSuite` helper ranks competing module variants and exports evaluation summaries to JSON files. It also supports feedback metrics structured for the GEPA optimizer. The skill targets DSPy version 3.2.1. Tool access is limited to `Read`, `Write`, `Glob`, and `Grep`.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add OmidZamani/dspy-skills --skill dspy-evaluation-suite -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/dspy-evaluation-suite/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/dspy-evaluation-suite/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add OmidZamani/dspy-skills --skill dspy-evaluation-suite -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/dspy-evaluation-suite/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add OmidZamani/dspy-skills --skill dspy-evaluation-suite -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/dspy-evaluation-suite/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install OmidZamani/dspy-skills dspy-evaluation-suite
```

2. **Manually: put the files in place**
- File: `.github/skills/dspy-evaluation-suite/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add OmidZamani/dspy-skills --skill dspy-evaluation-suite -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/OmidZamani/dspy-skills --path skills/dspy-evaluation-suite
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/dspy-evaluation-suite/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add OmidZamani/dspy-skills --skill dspy-evaluation-suite -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/OmidZamani/dspy-skills/tree/HEAD/skills/dspy-evaluation-suite/SKILL.md

## When to use it
- measure DSPy module performance before and after optimization
- compare multiple program candidates on validation examples
- run parallel batch evaluations using dspy.evaluate.Evaluate
- export detailed evaluation metrics to a JSON summary report

## Example prompts
- Run an evaluation on my DSPy pipeline using devset and answer_exact_match with 8 threads.
- Create a custom multi-factor metric checking correctness, length, and reasoning presence.
- Compare two DSPy module variants using EvaluationSuite and save the output to report.json.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 4ba38c4dd16f2d26.

BEGIN THIRD-PARTY FILE 4ba38c4dd16f2d26
````text
---
name: dspy-evaluation-suite
version: "1.0.0"
dspy-compatibility: "3.2.1"
tags: ["evaluation"]
requires-extras: []
description: Use for evaluating DSPy programs with Evaluate, answer_exact_match, SemanticF1, custom metrics, baselines, and program comparisons.
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
---

# DSPy Evaluation Suite

## Goal

Systematically evaluate DSPy programs using built-in and custom metrics with parallel execution.

## When to Use

- Measuring program performance before/after optimization
- Comparing different program variants
- Establishing baselines
- Validating production readiness

## Related Skills

- Use with any optimizer: [dspy-bootstrap-fewshot](../dspy-bootstrap-fewshot/SKILL.md), [dspy-miprov2-optimizer](../dspy-miprov2-optimizer/SKILL.md), [dspy-gepa-reflective](../dspy-gepa-reflective/SKILL.md)
- Evaluate RAG pipelines: [dspy-rag-pipeline](../dspy-rag-pipeline/SKILL.md)

## Inputs

| Input | Type | Description |
|-------|------|-------------|
| `program` | `dspy.Module` | Program to evaluate |
| `devset` | `list[dspy.Example]` | Evaluation examples |
| `metric` | `callable` | Scoring function |
| `num_threads` | `int` | Parallel threads |

## Outputs

| Output | Type | Description |
|--------|------|-------------|
| `score` | `float` | Average metric score |
| `results` | `list` | Per-example results |

## Workflow

### Phase 1: Setup Evaluator

```python
from dspy.evaluate import Evaluate

evaluator = Evaluate(
    devset=devset,
    metric=my_metric,
    num_threads=8,
    display_progress=True
)
```

### Phase 2: Run Evaluation

```python
result = evaluator(my_program)
print(f"Score: {result.score:.2f}%")
# Access individual results: (example, prediction, score) tuples
for example, pred, score in result.results[:3]:
    print(f"Example: {example.question[:50]}... Score: {score}")
```

## Built-in Metrics

### answer_exact_match

```python
import dspy

# Normalized, case-insensitive comparison
metric = dspy.evaluate.answer_exact_match
```

### SemanticF1

LLM-based semantic evaluation:

```python
from dspy.evaluate import SemanticF1

semantic = SemanticF1()
score = semantic(example, prediction)
```

## Custom Metrics

### Basic Metric

```python
def exact_match(example, pred, trace=None):
    """Returns bool, int, or float."""
    return example.answer.lower().strip() == pred.answer.lower().strip()
```

### Multi-Factor Metric

```python
def quality_metric(example, pred, trace=None):
    """Score based on multiple factors."""
    score = 0.0
    
    # Correctness (50%)
    if example.answer.lower() in pred.answer.lower():
        score += 0.5
    
    # Conciseness (25%)
    if len(pred.answer.split()) <= 20:
        score += 0.25
    
    # Has reasoning (25%)
    if hasattr(pred, 'reasoning') and pred.reasoning:
        score += 0.25
    
    return score
```

### GEPA-Compatible Metric

```python
def feedback_metric(example, pred, trace=None, pred_name=None, pred_trace=None):
    """Return a GEPA-compatible score and textual feedback."""
````
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